This paper studies an alternative bias correction for the M-estimator, which is obtained by correcting the moment … equations in the spirit of Firth (1993). In particular, this paper compares the stochastic expansions of the analytically-bias …-step bias correction by using the bias correction of moment equations. This finding suggests that the comparison between the one …

We assess the bias and the efficiency of state-of-the-art dynamic panel data estimators by means of model-based Monte …

This paper develops the approximate bias of the ordinary least squares estimator of the mean reversion parameter in … whether the initial condition is fixed or random. The approximate bias is used to construct a bias corrected estimator. The … performance of the approximate bias and the bias corrected estimator is examined using simulated data …

We investigate the finite-sample bias of the quasi-maximum likelihood estimator (QMLE) in spatial autoregressive models … with possible exogenous regressors. We derive the approximate bias result of the QMLE in terms of model parameters and also … the moments (up to order 4) of the error distribution, and thus a feasible bias-correction procedure is directly …

or missing variable bias), an important and intractable problem in many disciplines. The test is simple: one selects a …

This paper examines the effect of systematic self-report bias, the non-random deviation between the self-reported and … true values of the same measure. This bias may be constant or variable, and can mislead empirical analyses based on … measured data in the cross-section. A school nutrition policy changed the bias differentially in the treatment and control …

some markets and poorly in others, often yield negative or indeterminate estimates, and how to quantify estimation bias … empirically. Using the high-low spread estimator as our main setting, we show that the measure is biased due to two common bias … factors. These bias sources contribute to performance loss significantly more than idiosyncratic factors, including model …

nonlinearity of the problem. We present a penalty for the objective function that reduces the bias in the resulting point estimates … practice. The form of the penalty also provides interesting intuition into how the bias reduction is working. We present … simulation results that suggest that the penalized optimization approach may substantially reduce the bias in nonlinear fixed …